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The best way to get a sense for the AT&T assembly syntax is to look at an actual AT&T-style s file produced by gcc Doing this actually has two benefits: First of all, it will help you become familiar with the AT&T mnemonics and formatting conventions In addition, you may find it useful, when struggling to figure out how to call a C library function from assembly, to create a short C program that calls the function of interest and then examines the s file that gcc produces when it compiles your C program The dateisc program which follows was part of my early research, and I used it to get a sense for how ctime() was called at the assembly level Obviously, for this trick to work you must have at least a journeyman understanding of the AT&T mnemonics (I discuss ctime() and other time-related C library calls in detail in the next chapter) You don't automatically get a s file every time you compile a C program The s file is created, but once gas assembles the s file to a binary object code file (typically a o file), it deletes the s file If you want to examine a s file created by gcc, you must compile with the -S option (Note that this is an uppercase S Case matters big time in the Unix world!) The command would look like this: gcc dateisc -S Note that the output of this command is the assembly source file only If you specify the -S option, gcc understands that you want to generate assembly source rather than an xecutable program file, so all it will generate is the s file To compile a C program to an executable program file, you must compile it again without the -S option Here's dateisc It does nothing more than print out the date and time as returned by the standard C library function ctime(): #include <timeh> #include <stdioh> int main() { time_t timeval; (void)time(&timeval); printf("The date is: %s", ctime(&timeval)); exit(0); } It's not much of a program, but it does illustrate the use of three C library function calls, time(), ctime(), and printf() When gcc compiles the preceding program (dateisc), it produces the file dateiss, which follows I have manually added the equivalent Intel mnemonics as comments to the right of the AT&T mnemonics, so.

Polysemy refers to the concept of words with multiple meanings. ne should have a clear understanding of all the concepts when an ontology is formally speci ed. Here is an example of an inconsistent ontology which is caused by polysemy: (A married woman is a woman), MarriedWoman v :Divorcee (A married woman is not a divorcee), Divorcee v HadHusband u :HasHusband (A divorcee had a husband and has no husband), HasHusband v MarriedWoman (HasHusband means married), HadHusband v MarriedWoman (HadHusband means married). In the ontology speci cation above, the concepts Divorcee is unsatis able because of the misuse of the word MarriedWoman. Therefore, one has to carefully check if there is some misunderstanding with respect to concepts that have been used in the ontology, which may become rather dif cult when an ontology is large. MarriedWoman v Woman. INCONSISTENT ONTOLOGIES. Barcode Maker In .NET Using Barcode printer for Visual Studio .NET Control .Related: Word ITF-14 Generator , Make EAN 128 .NET WinForms , VB.NET ITF-14 Generator

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Reputation systems are, however, somewhat more general because the decisions to be made can come from multiple sensor streams and reputation scores can to some extent be abstracted away from the particular physical models Part of the commonality with the calibration problem is the need for an outlier detection protocol With strong prior information concerning the model of the physical system, model-based outlier detection can be employed in sensor networks [87, 136, 137] The reputation rating of data points is made proportional to its conformance to this model The model could be deterministic or statistical, or even constructed using adaptive techniques in a phase where all nodes are trusted (eg, following calibration) With only partial knowledge of the physical situation, one may apply simple criteria such as rejection of values outside particular ranges, or sequences which change too quickly, or sequences that change too little (indicating stuck-at faults).

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Apart from the dif culty of dealing with dynamic environments, model-based techniques suffer from the dif culty of modeling the fault behavior of nodes An alternative is to use consensus-based techniques, which use much weaker assumptions concerning models In particular, it is assumed that neighboring nodes have correlated readings and that nonfaulty nodes are the large majority of those still permitted to participate in decisions To plan a sensor node density suf cient for the rst of these conditions to be met of course requires some knowledge of the physical model In operation, readings are compared to the consensus of neighboring nodes in order to compute a reputation score, rather than being compared to the model predictions There are two basic consensus criteria: (a) outlier designation based on distance from the consensus or (b) density-based outlier detection.

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In distance-based mechanisms, the absolute difference in sensor readings from the (reputation-weighted) average of the remaining nodes is used as the basis for outlier rejection [138, 139] To set the outlier detection thresholds, some knowledge of the statistical model of the system (phenomenon and sensor behavior) is thus required Density-based outlier rejection [140, 141] operates similarly, except that now the data can be clustered into groups with differing spreads, for which different distance thresholds then apply Any outlier detection scheme [142] can be used to produce the reputation score Having accomplished this, there are then several possibilities for how these scores will be accumulated and used One can assign scores from any transaction as being binary (satisfactory/unsatisfactory) or give more levels A particularly convenient framework is to accumulate reputation metrics using a beta distribution [96, 143] since only two parameters are needed to characterize the entire probability distribution.

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